Brain tumors are also known as intracranial disease which is occurred due to cause of uncontrolled cell growth in the brain. Detecting and classifying the brain tumors at the initial stage plays a crucial role in saving the patient’s life. Radiologist uses MRI scans to identify and classify the various types of brain tumors in a manual approach. However, it is inaccurate and time-consuming with the large number of images. Among machine learning convolutional neural network (CNN) is one kind of significant algorithm that can extract the features automatically with high accuracy. The drawback of this algorithm is that it can extract features without knowing micro and macro features which occurs overfitting. The proposed architecture of Parallel CNN (PCNN) can extract the features by knowing the micro and macro features from two separate window sizes. At first, augmenting the normalized data using geometric transformation to enhance the number of images. Then, Micro and macro features are extracted using the proposed architecture PCNN alongside batch normalization to reduce the overfitting problem. Finally, three types of tumors glioma, meningioma, and pituitary are classified using various types of classifiers like Softmax, KNN, and SVM. The proposed DPCNN –SVM obtained the best accuracy 96.7% with the special features compared with the other pertained model.